Ochratoxin A (OTA) is a potent pentaketide nephrotoxin diffusely distributed in food and feed products; it is also carcinogenic, neurotoxic, teratogenic and immunotoxic. The mycotoxin is produced by species of genus Aspergillus and Penicillium. OTA is the primarily mycotoxin risk in wine and dried wine fruits, and the main source of OTA contamination in grapes is A. carbonarius, followed by A. niger and A. welwitschiae. Geographical regions, climatic conditions, crop and pest management, and grape genotypes influence the contamination risk. So, the availability of validate predictive models could be very useful in supporting the optimization of grape management for the mitigation of OTA content in grapes. So far, the only predictive model for OTA risk in grapes is that developed by Battilani et al. [1]. It uses hourly data on air temperature, relative humidity and rainfall as inputs and provides a risk assessment during the growing season. The model has not yet been validated with real field data. In this framework, the OTA model was implemented in a digital platform, developed within the project "Digital Grape", aiming at supporting the management of main agronomic and phytosanitary practices for precision viticulture (https://digitalgrape.it/). The model was elaborated and preliminary tested with experimental data, and successively used to predict OTA risk on 43 Apulian vineyards in two consecutive years (2021-2022). Grape samples were collected at harvest from each field and analyzed for OTA content and A. carbonarius contamination. The results were compared with the Toxin Index generated by the OTA model at harvest time. Chemical analysis evidenced for both years an OTA content always below the legal limit of 2 µg/Kg [2] and A. carbonarius contamination ranging from 0 to 1,1 x 107 CFU/g must, while the Toxin Index varied from 75 to 6097. In general, the correlation between the Toxin Index and the OTA level was low, mainly for the vineyards located in Salento (South of Apulia), while a higher correlation was observed for vineyards in North of Apulia, especially in 2022. On the opposite, a good correlation between the OTA level and A. carbonarius contamination was observed in most of the vineyards in both years. These preliminary results highlighted that the OTA predictive model need to be improved through fine-tuning on experimental data for being useful in managing OTA risk in a smart agriculture system. References 1.Battilani, Paola, and Marco Camardo Leggieri. "OTA-grapes: a mechanistic model to predict ochratoxin A risk in grapes, a step beyond the systems approach." Toxins 7.8 (2015): 3012-3029. 2.Commission Regulation (EC) No 1881/2006 of 19 December 2006 setting maximum levels for certain contaminants in foodstuffs. Aknowledgements This work was partially funded by Puglia Region - Project "Digital Grape - New Digital Technologies and Decision Support Systems for the Improvement of Quality and Sustainability in Viticulture" - P.S.R. Puglia 2014/2020 - Misura 16 - Cooperazione - Sottomisura 16.2 "Sostegno a progetti pilota e allo sviluppo di nuovi prodotti, pratiche, processi e tecnologie".
Implementation in a digital platform and validation of an Ochratoxin A prediction model: 2-year trials in vineyards located in Apulia (Southern Italy)
Giancarlo Perrone;Katia Gialluisi;
2023
Abstract
Ochratoxin A (OTA) is a potent pentaketide nephrotoxin diffusely distributed in food and feed products; it is also carcinogenic, neurotoxic, teratogenic and immunotoxic. The mycotoxin is produced by species of genus Aspergillus and Penicillium. OTA is the primarily mycotoxin risk in wine and dried wine fruits, and the main source of OTA contamination in grapes is A. carbonarius, followed by A. niger and A. welwitschiae. Geographical regions, climatic conditions, crop and pest management, and grape genotypes influence the contamination risk. So, the availability of validate predictive models could be very useful in supporting the optimization of grape management for the mitigation of OTA content in grapes. So far, the only predictive model for OTA risk in grapes is that developed by Battilani et al. [1]. It uses hourly data on air temperature, relative humidity and rainfall as inputs and provides a risk assessment during the growing season. The model has not yet been validated with real field data. In this framework, the OTA model was implemented in a digital platform, developed within the project "Digital Grape", aiming at supporting the management of main agronomic and phytosanitary practices for precision viticulture (https://digitalgrape.it/). The model was elaborated and preliminary tested with experimental data, and successively used to predict OTA risk on 43 Apulian vineyards in two consecutive years (2021-2022). Grape samples were collected at harvest from each field and analyzed for OTA content and A. carbonarius contamination. The results were compared with the Toxin Index generated by the OTA model at harvest time. Chemical analysis evidenced for both years an OTA content always below the legal limit of 2 µg/Kg [2] and A. carbonarius contamination ranging from 0 to 1,1 x 107 CFU/g must, while the Toxin Index varied from 75 to 6097. In general, the correlation between the Toxin Index and the OTA level was low, mainly for the vineyards located in Salento (South of Apulia), while a higher correlation was observed for vineyards in North of Apulia, especially in 2022. On the opposite, a good correlation between the OTA level and A. carbonarius contamination was observed in most of the vineyards in both years. These preliminary results highlighted that the OTA predictive model need to be improved through fine-tuning on experimental data for being useful in managing OTA risk in a smart agriculture system. References 1.Battilani, Paola, and Marco Camardo Leggieri. "OTA-grapes: a mechanistic model to predict ochratoxin A risk in grapes, a step beyond the systems approach." Toxins 7.8 (2015): 3012-3029. 2.Commission Regulation (EC) No 1881/2006 of 19 December 2006 setting maximum levels for certain contaminants in foodstuffs. Aknowledgements This work was partially funded by Puglia Region - Project "Digital Grape - New Digital Technologies and Decision Support Systems for the Improvement of Quality and Sustainability in Viticulture" - P.S.R. Puglia 2014/2020 - Misura 16 - Cooperazione - Sottomisura 16.2 "Sostegno a progetti pilota e allo sviluppo di nuovi prodotti, pratiche, processi e tecnologie".I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
